unsloth/Ministral-3-3B-Base-2512

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Ministral 3 3B Base 2512

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

This model is the base pre-trained version, not fine-tuned for instruction or reasoning tasks, making it ideal for custom post-training processes. For instruction and chat based use cases, we recommend using Ministral 3 3B Instruct 2512.

The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized.

Key Features

Ministral 3 3B consists of two main architectural components:

• —3.4B Language Model
• —0.4B Vision Encoder

The Ministral 3 3B Base model offers the following capabilities:

• —Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
• —Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
• —Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
• —Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
• —Large Context Window: Supports a 256k context window.

Use Cases

Ideal for lightweight, real-time applications on edge or low-resource devices, such as:

• —Image captioning
• —Text classification
• —Real-time efficient translation
• —Data extraction
• —Short content generation
• —Fine-tuning and specialization
• —And more...

Bringing advanced AI capabilities to edge and distributed environments for embedded systems.

Ministral 3 Family

Other formats available here.

Benchmark Results

We compare Ministral 3 to similar sized models.

Reasoning

Instruct

Base

Usage

The model can be used with the following frameworks;

• —`vllm`: See here
• —`transformers`: See here

vLLM

We recommend using this model with vLLM.

Installation

Make sure to install most recent vllm:

uv pip install -U vllm \
    --torch-backend=auto \
    --extra-index-url https://wheels.vllm.ai/nightly

Doing so should automatically install `mistral_common >= 1.8.6`.

To check:

python -c "import mistral_common; print(mistral_common.__version__)"

You can also make use of a ready-to-go docker image or on the docker hub.

Serve

Due to their size and the BF16 format of their weights Ministral-3-3B-Base-2512 and Ministral-3-8B-Base-2512 can run on a single 1xH200 GPU.

A simple launch command is:

vllm serve mistralai/Ministral-3-3B-Base-2512 \
  --tokenizer_mode mistral --config_format mistral --load_format mistral

Additional flags:

• —You can set --max-model-len to preserve memory. By default it is set to 262144 which is quite large but not necessary for most scenarios.
• —You can set --max-num-batched-tokens to balance throughput and latency, higher means higher throughput but higher latency.

Usage of the model

Here we asumme that the model mistralai/Ministral-3-3B-Base-2512 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.

<details> <summary>Test Base</summary>

Quick test with the base model.

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 256

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

response = client.completions.create(
    model=model,
    prompt="What is the best thing in the universe ?",
    temperature=TEMP,
    max_tokens=MAX_TOK,
)

print(response.choices[0].text)

</details>

Transformers

You can also use Ministral 3 3B Base 2512 with Transformers ! Make sure to install Transformers from its first v5 release candidate or from "main":

pip install transformers==5.0.0rc0

To make the best use of our model with Transformers make sure to have installed mistral-common >= 1.8.6 to use our tokenizer.

pip install mistral-common --upgrade

Then load our tokenizer along with the model and generate:

<details> <summary>Python snippet</summary>

from transformers import Mistral3ForConditionalGeneration, MistralCommonBackend, FineGrainedFP8Config

model_id = "mistralai/Ministral-3-3B-Base-2512"
model = Mistral3ForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
)
tokenizer = MistralCommonBackend.from_pretrained(model_id)

input_ids = tokenizer.encode("Once about a time, France was a", return_tensors="pt")
input_ids = input_ids.to("cuda")

output = model.generate(
    input_ids,
    max_new_tokens=30,
)[0]

decoded_output = tokenizer.decode(output[len(input_ids[0]):])
print(decoded_output)

</details>

License

This model is licensed under the Apache 2.0 License.

You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.